Super-resolution reconstruction proposes a fusion of several low quality images into one higher quality result with better optical resolution. Classic super resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inaccurately, as often happens for non-global motion fields, annoying artifacts appear in the super-resolved outcome. Encouraged by recent developments on the video denoising problem, where state-of-the-art algorithms are formed with no explicit motion estimation, we seek a super-resolution algorithm of similar nature that will allow processing sequences with general motion patterns. In this talk we base our solution on the Non-Local-Means (NLM) algorithm. We show how this denoising method is generalized to become a relatively simple super-resolution algorithm with no explicit motion estimation. Results on several test movies show that the proposed method is very successful in providing super-resolution on general sequences.
In this talk we consider several inverse problems in image processing, using sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm for gray-level images with state-of-the-art denoising performance. We then extend these results to color images, handling their denoising, inpainting, and demosaicing. Following the above ideas, with necessary modifications to avoid color artifacts and over-fitting, we present stat-of-the art results in each of these applications. Another extension considered is video denoising — we demonstrate how the above method can be extended to work with 3D patches, propagate the dictionary from one frame to another, and get both improved denoising performance while also reducing substantially the computational load per pixel.
The super-resolution reconstruction problem addresses the fusion of several low quality images into one higher-resolution outcome. A typical scenario for such a process could be the fusion of several video fields into a higher resolution output that can lead to high quality printout. The super-resolution result provides TRUE resolution, as opposed to the typically used interpolation techniques. The core idea behind this ability is the fact that higher-frequencies exist in the measurements, although in an aliased form, and those can be recovered due to the motion between the frames. Ever since the pioneering work by Tsai and Huang (1984), who demonstrated the core ability to get super-resolution, much work has been devoted by various research groups to this problem and ways to solve it. In this talk I intend to present the core ideas behind the super-resolution (SR) problem, and our very recent results in this field. Starting form the problem modeling, and posing the super-resolution task as a general inverse problem interpretation, we shall see how the SR problem can be addressed effectively using ML and later MAP estimation methods. This talk also show various ingredients that are added to the reconstruction process to make it robust and efficient. Many results will accompany these descriptions, so as to show the strengths of the methods.
The super-resolution reconstruction process deals with the fusion of several low quality and low-resolution images into one higher-resolution and possibly better final image. We start by showing that from theoretic point of view, this fusion process is based on generalized sampling theorems due to Yen (1956) and Papulis (1977). When more realistic scenario is considered with blur, arbitrary motion, and additive noise, an estimation approach is considered instead.
We describe methods based on the Maximum-Likelihood (ML), Maximum-A-posteriori Probability (MAP), and the Projection onto Convex Sets POCS) as candidate tools to use. Underlying all these methods is the development of a model describing the relation between the measurements (low-quality images) and the desired output (high-resolution image). Through this path we presents the basic rational behind super-resolution, and then present the dichotomy between the static and the dynamic super-resolution process. We proposed treatment of both, and deal with several interesting special cases.